NBER WORKING PAPER SERIES

THE EFFECTS OF THE AFFORDABLE CARE ACT ON HEALTH INSURANCE COVERAGE AND LABOR MARKET OUTCOMES

Mark DugganGopi Shah GodaEmilie Jackson

Working Paper 23607http://www.nber.org/papers/w23607

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138July 2017

We would like to thank Paula Gablenz and Audrey Guo for exceptional research assistance, and Alex Gelber, Kosali Simon, and seminar participants at the University of Wisconsin, UC Berkeley, UC Irvine, UC Davis, University of Iowa, University of Chicago, Michigan State University and the SIEPR-Sloan Working Longer conference for helpful comments. We thank the Alfred P. Sloan Foundation for generous support. The content is solely the responsibility of the authors and not Stanford University or the NBER. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

© 2017 by Mark Duggan, Gopi Shah Goda, and Emilie Jackson. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

The Effects of the Affordable Care Act on Health Insurance Coverage and Labor Market OutcomesMark Duggan, Gopi Shah Goda, and Emilie JacksonNBER Working Paper No. 23607July 2017JEL No. H31,H51,J18,J20,J38

ABSTRACT

The Affordable Care Act (ACA) includes several provisions designed to expand insurance coverage that also alter the tie between employment and health insurance. In this paper, we exploit variation across geographic areas in the potential impact of the ACA to estimate its effect on health insurance coverage and labor market outcomes in the first two years after the implementation of its main features. Our measures of potential ACA impact come from pre-existing population shares of uninsured individuals within income groups that were targeted by Medicaid expansions and federal subsidies for private health insurance, interacted with each state’s Medicaid expansion status. Our findings indicate that the majority of the increase in health insurance coverage since 2013 is due to the ACA and that areas in which the potential Medicaid and exchange enrollments were higher saw substantially larger increases in coverage. While labor market outcomes in the aggregate were not significantly affected, our results indicate that labor force participation reductions in areas with higher potential exchange enrollment were offset by increases in labor force participation in areas with higher potential Medicaid enrollment

Mark DugganStanford UniversityDepartment of Economics579 Serra MallStanford, CA 94305-6072and NBERmgduggan@stanford.edu

Gopi Shah GodaStanford UniversitySIEPR366 Galvez St.Stanford, CA 94305and NBERgopi@stanford.edu

Emilie JacksonStanford University579 Serra Mall Stanford, CA 94305 emilyj91@stanford.edu

2

I. INTRODUCTION

The Affordable Care Act (ACA), passed in 2010, represents the largest reform to the U.S. health

care system since the introduction of Medicare and Medicaid in 1965. One of its primary goals was to

reduce the number of uninsured, which had hovered around one-sixth of the non-elderly population during

the prior decade. Several provisions of the law aimed to achieve this goal, including expansions of the

Medicaid program to cover low-income individuals, private health insurance subsidies provided to

individuals with family incomes between 100 and 400% of the federal poverty line (FPL), mandates on

employers to offer health insurance coverage to employees, and penalties imposed on individuals without

insurance. After many of these provisions were implemented in January of 2014, uninsurance rates among

non-elderly adults fell substantially, from 20.1 percent in the fourth quarter of 2013 to 15.1 percent by the

fourth quarter of 2014 and 12.6 percent by the fourth quarter of 2015, as shown in Figure 1.1 However,

these aggregate patterns do not indicate how insurance coverage would have evolved in the absence of the

ACA.

Many of these same provisions also serve to weaken the tie between employment and health

insurance coverage, and therefore may affect both labor supply and demand. Consistent with this, the

Congressional Budget Office (CBO) has estimated that the ACA will reduce the size of the labor force by

1.5 to 2 percent (2 to 2.5 million individuals) by 2024 (CBO 2014). While the primary channels for this

projected effect are through the incentive effects resulting from the availability of subsidies for private

health insurance coverage and the expanded coverage for the Medicaid program that could reduce labor

supply, there is also scope for effects on employment through policies that could impact labor demand.

Aggregate labor force participation rates do not show any significant changes after the legislation was

passed in 2010 or after key provisions were implemented in early 2014 (see Figure 2). But again, these

aggregate rates do not indicate how labor force participation would have changed if the ACA had not been

implemented.

In this paper, we examine how the ACA affected health insurance coverage and labor market

outcomes in the two years after the key provisions of the ACA took effect on January 1, 2014. Because the

1 Source: National Center for Health Statistics. Health Insurance Coverage: Early Release of Quarterly Estimates

from the National Health Interview Survey, January 2010 – March 2016, Table 3, retrieved on September 12, 2016.

Available at: http://www.cdc.gov/nchs/data/nhis/earlyrelease/quarterly_estimates_2010_2016_q11.pdf.

3

ACA is a national reform and affected all states, it is difficult to disentangle the effects of the law from

other changes that would have happened without it. For example, employment growth in the first year after

the ACA averaged 241 thousand per month, which was the most rapid pace since the late 1990s and 30

percent greater than the average growth in the preceding three years. It is plausible that health insurance

coverage would have risen substantially even in the absence of the ACA. Similarly, as shown in Figure 1,

the fraction of non-elderly adults without health insurance was trending down even prior to 2014. Thus,

how much of the increase in health insurance coverage was driven by the ACA versus other factors is

ultimately an empirical question.

To investigate this issue, we use individual-level data from the American Community Survey

(ACS) to exploit geographic variation in the potential impact of the ACA. In January 2014, ACA expanded

Medicaid to individuals in families with incomes below 138% of the federal poverty line (FPL). However,

only about half of all states initially proceeded with the Medicaid expansion, and thus the overall effect of

this provision on coverage may be much smaller as a result. Additionally, the ACA provided subsidies to

purchase private health insurance through state health insurance exchanges to individuals with incomes

between 100% and 400% of FPL. To the extent that these provisions raised health insurance coverage, one

would expect areas with a higher fraction of the population both uninsured and below 400% FPL to

experience larger increases in insurance coverage. However, the magnitude of the effects may depend on

whether the state proceeded with the Medicaid expansion. More specifically, an area with a substantial

fraction both uninsured and below 138% FPL may see a relatively small coverage increase if the state did

not expand Medicaid. Similarly, individuals with incomes between 100 and 138% of FPL might be on

Medicaid in expanding states and privately insured in states that did not expand.

Our data allow us to estimate the share of each area that was (a) uninsured and below 138 percent

of the poverty line and (b) uninsured and between 139 and 399 percent of the federal poverty line in 2010-

2013, just prior to the implementation of the ACA’s key provisions.2 The large sample size in the ACS

2 Other provisions that expanded health insurance to adults under the age of 26 through their parents’ private health

insurance plans took effect three years earlier in 2011 and also increased coverage (Antwi, Moriya and Simon 2013).

4

along with the geographic identifiers allows us to divide the U.S. into more than 1,000 areas known as

Public Use Microdata Areas (PUMAs).3

Theoretically, one would expect areas with a large share uninsured and below 400% of FPL prior

to ACA implementation to experience larger increases in health insurance coverage beginning in 2014. The

effect for the group below 138% FPL would likely be sensitive to whether the state had expanded Medicaid.

We empirically test these predictions using annual ACS data from the 2010 to 2015 period exploiting

variation across PUMAs. Our methodology is similar to that used by previous work to estimate the effect

of the introduction of Medicare in 1965 (Finkelstein, 2007; Finkelstein and McKnight, 2008) and of the

2006 Massachusetts Health Reform (Kolstad and Kowalski, 2012; Miller, 2012). However, our sample size

and level of geographic detail allow us to use much more granular variation than in this previous work.

Our results indicate that the ACA had a substantial impact on overall health insurance coverage.

For areas with average characteristics prior to the ACA, we estimate that health insurance coverage

increased by 4.2 percentage points in states that expanded Medicaid, and 2.6 percentage points in states that

did not, as a result of this legislation. These changes amplified the difference in coverage between the two

groups of states given that 19 percent of non-elderly adults were uninsured just prior to ACA

implementation in states that expanded Medicaid versus 23 percent in states that did not. Additionally, the

ACA-induced coverage increases are generally larger in 2015 than in 2014.

We also find evidence that regions with lower levels of baseline insurance coverage and/or higher

shares of the population eligible for Medicaid coverage or financial assistance to purchase insurance saw

larger increases in coverage. In particular, in regions where the share of the population less than 138% FPL

and uninsured is 10 percentage points higher (approximately 2.5 standard deviations), the coverage increase

was 4 percentage points higher in expansion states but not significantly higher in non-expansion states. In

regions where the share of the population between 139% and 399% FPL and uninsured is 10 percentage

points higher, the coverage increase was 2.5 percentage points higher in non-expansion states and the

increase in expansion states was somewhat lower but not significantly different.

3 While there are more than 2,000 PUMAs, we focus on a consistent set of boundaries across census years which

results in fewer PUMAs of larger average size.

5

Data on the source of coverage allow us to understand the channels through which health insurance

is increasing. We find that increases in Medicaid coverage accounted for the majority of coverage increases

in expansion states, while privately-purchased health insurance, including policies purchased on the ACA

exchanges, accounted for the majority in non-expansion states. We find little evidence that private health

insurance from employers changes after the ACA is implemented in either expansion or non-expansion

states, suggesting that there was little crowdout of this coverage.

We also investigate the impact of the ACA on non-elderly adults’ labor market outcomes. These

effects are also empirically ambiguous. On one hand, individuals might reduce their labor supply in

response to incentive effects (as subsidies to purchase private insurance decline with income and the

provision of Medicaid to those with lower incomes may reduce the incentive to work) or income effects (as

health insurance is valuable and individuals may respond to this increase in effective income by working

less). The availability of less costly health insurance outside of employment may lead near-elderly workers

to retire earlier or shift to part-time employment. On the other hand, some may value the option of obtaining

private health insurance and increase their work effort to qualify for the subsidies, which are especially

large at incomes just above the poverty line. Additionally, increases in insurance coverage may increase

the demand for labor in areas that previously had a large share uninsured.4 For example, hospitals might

need to hire more nurses if the demand for their services grows.

Our findings indicate that the average labor supply effects of the ACA were close to zero but that

this average masks important heterogeneity in its effects. More specifically, we find that in areas with a

high share uninsured and eligible for private insurance subsidies, labor force participation fell significantly.

In contrast, in areas with a high share uninsured but with incomes too low to qualify for private insurance

subsidies, labor force participation increased significantly. These changes suggest that middle-income

individuals reduced their labor supply due to the additional tax on earnings while lower income individuals

worked more in order to qualify for private insurance. In the aggregate, these countervailing effects

approximately balance.

4 Chodorow-Reich et al. (2012) find that expansions in Medicaid funding from the American Recovery and

Reinvestment Act led to substantial increases in employment in the health care sector.

6

Our paper builds on a growing literature on the effects of the ACA on health insurance coverage

and labor market outcomes. Previous literature suggests that the ACA substantially increased health

insurance coverage (Sommers et al. 2014, 2015; Courtemanche et al. 2017, Frean et al. 2017) but that it had

little impact on the labor market (Buchmueller et al, 2015; Kaestner et al, 2015; Leung and Mas, 2016;

Gooptu et al. 2016; Moriya et al. 2016; Gustman et al. 2016; Abraham and Royalty 2017).5 However,

research on the policy’s labor market effects has not explored how the ACA-induced increases in coverage

vary by area based on income. Our results highlight the importance of using the underlying income

distribution of an area to consider the possible heterogeneous response to this policy.

The rest of the paper proceeds as follows. Section II contains background information on the key

features of the ACA for our analysis and describes the related literature. In Section III, we describe our

empirical strategy. Section IV provides details regarding the data we use in the analysis including average

characteristics prior to ACA implementation. We report our results in Section V, robustness exercises in

Section VI, and Section VII concludes.

II. BACKGROUND

In this section, we describe various features of the Affordable Care Act designed to increase health

insurance coverage, the primary channels through which this legislation may influence the labor market,

and previous literature relevant to the impacts of similar policies on health insurance coverage and the tie

between health insurance and labor market outcomes.

A. The Affordable Care Act

5 Other studies have examined the effects on employer-sponsored health insurance (Blavin et al, 2015; Abraham et al.

2016) and the effects of the dependent care mandate that took effect in late 2010 (Cantor et al. 2012; Sommers and

Kronick 2012; Antwi et al 2013, Bailey and Chorniy, 2015; Heim et al, 2015). In addition to these studies, several

descriptive analyses have used a variety of novel data sources to document the ACA’s impact on coverage (Long et

al. 2014; Smith and Medalia 2014; Carman et al. 2015; Black and Cohen 2015; Courtemanche et al. 2016) and some

authors have modeled the impact of the ACA on labor supply (Heim, Hunter, Lurie, and Ramnath 2014; Mulligan

2014, 2015a, 2015b; Fang and Shephard 2015). Also, note that some studies have found evidence of changes in labor

demand in occupations and industries most affected by the employer shared responsibility requirement, resulting in

an increase in involuntary part-time work (Even and Macpherson 2016; Dillender et al. 2016).

7

The Affordable Care Act includes dozens of provisions to expand health insurance coverage, slow

the growth rate in health care costs, and reform the market for private health insurance. In this section, we

focus on the provisions designed to expand health insurance coverage.

The Medicaid Expansion

Just prior to the passage of the Affordable Care Act, the federal-state Medicaid program provided

health insurance to 57 million people.6 There was substantial variation across states with respect to which

individuals were eligible, what health care services were covered, the generosity of reimbursement to

providers, and the role of private managed care organizations (Duggan and Hayford 2013). This program

is means-tested and there is virtually no cost-sharing, with Medicaid premiums essentially equal to zero in

most states. Medicaid provides valuable protection to many of the nation’s most vulnerable residents, with

many studies finding that it improves health outcomes (e.g., Currie and Gruber, 1996, Sommers et al. 2012)

and enhances economic well-being (Baicker et al. 2014).

The Affordable Care Act substantially expanded eligibility for the Medicaid program. More

specifically, all individuals with family incomes less than or equal to 138 percent of the poverty-line

(adjusted for family size) would become eligible for the program. In early 2010 prior to ACA’s passage,

virtually all states already covered children in this income range, and thus the ACA’s primary effect was to

expand eligibility among non-elderly adults. Projections from CBO initially estimated that Medicaid

enrollment would – by 2016 – increase by 16 million as a result of the ACA. This represented half of the

increase in projected insurance coverage resulting from the ACA. However, this projection was later

reduced to just 11 million after the 2012 Supreme Court decision made it optional for states to move forward

with the Medicaid expansion and many states elected not to do so (CBO 2015).7

The actual impact of the ACA Medicaid expansion is likely to vary substantially across states for

three reasons. First, only 25 states (including the District of Columbia) chose to expand Medicaid by

January 2014 following the Supreme Court decision, as shown in Figure 3.8 As a result, while the number

6 These data and state-by-state enrollment data were obtained from https://www.medicaid.gov/medicaid/program-

information/medicaid-and-chip-enrollment-data/report-highlights/index.html in March 2017. 7 On June 28, 2012, the Supreme Court ruled that the ACA’s Medicaid expansion was “unconstitutionally coercive,”

and that the appropriate remedy was to constrain the Federal government’s power in enforcing state compliance. 8 As of July 2016, 7 additional states have followed suit, bringing the total to 32 states.

8

of Medicaid recipients nationally increased from 57 million in July-September 2013 to 74.8 million in

November 2016, the increase was substantially greater among states that elected to expand their Medicaid

programs.9 A second reason for a differential effect is that some states already covered a substantial fraction

of adults below 133 percent of the poverty line at the time the ACA was passed. All else equal, the likely

increase in coverage would be smaller in these states.10 A third reason that the effect of the Medicaid

expansion is likely to vary across states is that the fraction of individuals in poverty differs substantially

across states. Because of this, any expansion of Medicaid coverage would, all else equal, lead to a larger

increase in insurance coverage in states with high rates of poverty.11

Subsidized Coverage through State or the Federal Health Insurance Exchanges

Subsidies for private health insurance purchased on the ACA exchanges are calculated by first

determining a family’s (modified adjusted gross) income12 as a percentage of the federal poverty line. This

percentage maps to a maximum percentage of income that one is responsible for paying towards the cost

of health insurance, ranging from just 2 percent (at low levels of income) to 9.5 percent (from 300 to 400

percent of the poverty line). The subsidy level is the cost of the second lowest cost “silver tier” plan

available on the exchange less the maximum premium payment the person is responsible for. Once a

family’s income exceeds 400 percent of the poverty line, members are no longer eligible for subsidies for

purchasing health insurance through the exchanges. If a family earns less than 100 percent of the poverty

line, members were expected to be covered by Medicaid and therefore do not receive a subsidy (even in

non-expansion states). Therefore, subsidies vary considerably by income. Premiums also vary

9 Consider the contrast between California, which did expand its Medicaid program, and Texas, which did not. In

California, Medicaid enrollment increased by 57 percent (to 12.2 million) from September 2013 to November 2016.

In contrast, in Texas, enrollment rose just 8.2 percent over the same time period to 4.8 million. 10 For example, non-disabled childless adults in California were not eligible for Medicaid in 2013 regardless of income

level. In contrast, their counterparts in New York were eligible if their incomes were below the poverty line. This may

partially explain why California’s 57 percent increase in Medicaid enrollment since 2013 is much larger than New

York’s 13 percent increase (to 6.4 million) despite the fact that both expanded their programs as a result of the ACA. 11 Consider the difference between New Mexico and Colorado, two states that expanded Medicaid as a result of the

ACA. In 2013 just prior to this coverage expansion, 22 percent of New Mexico’s residents had family incomes below

the poverty line. In contrast, just 13 percent of Colorado residents were below the poverty line in that same year.

Because of this difference, one would expect the ACA to have a greater impact on Medicaid enrollment in New

Mexico. Consistent with this, the fraction of New Mexico residents with Medicaid coverage increased by substantially

more in New Mexico (from 22 to 36 percent) than in Colorado (from 15 to 25 percent) from 2013 to 2016. 12 Gross income includes salary, investment, and business income. Qualified deductions such as student loan interest

and IRA contributions are subtracted to arrive at adjusted gross income (AGI). Tax-exempt interest income is added

to AGI to arrive at modified adjusted gross income (MAGI).

9

considerably across states and counties and this leads to a large divergence in the effective subsidy available

to individuals in high- vs. low-cost areas. The value of the subsidy for different levels of income for an

individual 60 years of age is shown in Appendix Figure A.1, and the geographic variation in the subsidy

level is shown in Appendix Figure A.2.

Just as the growth in Medicaid enrollment since 2013 has varied substantially across states, so too

has the increase in coverage through the health insurance exchanges.13 The variation in Medicaid enrollment

combined with variation in exchange enrollment has led to differential changes in the share uninsured by

state, ranging from an estimated 0.7 percentage point reduction in Virginia to 12.9 percent reductions in

both Arkansas and Kentucky (see Appendix Table A.1).

B. The Affordable Care Act and the Labor Market

The Congressional Budget Office (CBO) has estimated that the ACA will reduce the size of the

labor force by 1.5 to 2 percent (2 to 2.5 million individuals) by 2024 (CBO 2014). The two primary channels

for this effect are the incentive effects resulting from the availability of subsidies for private health insurance

coverage and the expanded coverage for the Medicaid program.

Because Medicaid provides a source of insurance coverage regardless of employment, and its

means-tested nature may result in a high marginal tax on working, Medicaid expansions could affect labor

market outcomes by reducing the amount of labor supplied by workers. A limited recent literature has

examined the effect of Medicaid expansions or contractions on employment outcomes. In particular,

Garthwaite, Gross and Notowidigdo (2014) examine a contraction of Medicaid in Tennessee that led to a

marked increase in employment in Tennessee; Dague, DeLeire and Leininger (2014) and Baicker et al.

(2014) find more modest effects of Medicaid on employment in Wisconsin and Oregon, respectively. Since

these expansions or contractions were often targeted to childless adults, similar to the ACA, their

experiences are valuable in informing expectations of the effects of ACA Medicaid expansions on

employment. However, the difference in estimates leave considerable ambiguity regarding the expected

13 For example, despite having a similar number of non-elderly individuals, there are 1.09 million residents of Texas

enrolled in the state’s ACA exchange versus just 0.22 million in New York’s. This partly reflects the substantially

higher fraction uninsured in Texas in 2013, but this is not the only driver of the difference. For example, while Florida

has a smaller population and a smaller share uninsured than Texas in 2013, there are substantially more Florida

residents enrolled in the exchange (1.44 million versus 0.96 million). Exchange enrollment is available by state as of

February 2017 from http://kff.org/health-reform/state-indicator/total-marketplace-enrollment-and-financial-

assistance/.

10

effects of the ACA’s Medicaid expansions on labor market outcomes. Additionally, Medicaid’s effect may

be different with the ACA because – in contrast to the pre-ACA setting - a person would remain eligible

for private health insurance subsidies even if his/her income rose above the Medicaid-eligible threshold.

The private health insurance exchanges could affect labor market outcomes through several

channels. First, prior to the ACA, it is likely that some near-elderly workers were continuing to work until

they were eligible for Medicare at age 65 because they obtained health insurance through their employer.

Because it is less expensive for individuals to purchase coverage outside of employment, some may elect

to retire or shift to part-time work sooner as a result of the ACA. Second, the magnitude of the subsidy

declines with income. As a worker’s income rises from, for example, 133% of the Federal Poverty Line

(FPL) to 399% of the FPL, there is an effective tax rate of 12 percent on additional earnings. This may

reduce the incentive to work, causing some to scale back their hours or to shift to another job with lower

earnings (or to drop out of the labor force if their spouse is working). Third and as shown in Appendix

Figures A.1 and A.2, there exists a “cliff” in the subsidy at 400 percent of the federal poverty level, at which

point the subsidy drops to zero. Fourth, the availability of subsidized coverage in the exchange may

encourage workers to shift to smaller firms (which are less likely to offer coverage) or to start their own

businesses. Indeed just prior to the Affordable Care Act, workers in firms with fewer than 10 employees

were three times more likely than their counterparts in firms with 1,000 or more employees to be uninsured.

This difference was partially driven by the substantially higher health insurance premiums that small firms

or the self-employed tended to face relative to their larger counterparts (Gabel et al., 2006).

In addition, it is important to note that while many of the ACAs provisions may be theoretically

expected to reduce labor supply, one possible mechanism working in the opposite direction is that those

under 100% FPL in non-expansion states receive subsidized health insurance from the exchanges only if

they earn more than the federal poverty line and are not eligible for Medicaid unless they fall in certain

coverage groups. Relatedly, individuals may prefer private coverage to Medicaid, and therefore increase

their earnings to qualify for private coverage even in expansion states. Finally, expansions in coverage may

improve health, which could in turn reduce the disutility of work. These effects may not be instantaneous

but might instead grow over time as the duration of exposure to additional insurance coverage increased.

11

While many of the channels mentioned above relate to labor supply decisions, the ACA may also

drive firm behavior. For example, the ACA’s employer mandate applies to firms with 50 or more full-time

employees, and may therefore encourage some firms to stay below that threshold or to hire more part-time

workers. The ACA’s Small Business Tax Credit encourages firms with 25 or fewer employees and with

low-wage workers to provide health insurance coverage to their employees. This tax credit may lead to an

increase in firm offering and may therefore make jobs at these smaller employers more attractive. Related

to this, if smaller firms that previously offered coverage can drop it while sending their workers to the

exchange, this could lead to increases in wages and in employment as well. A less appreciated feature of

the ACA is that it may give some employers an incentive to “contract out” for low-wage workers because

the existing ESI system (with its large tax subsidies for high income workers) may be more attractive for

high-wage workers while coverage through the exchanges (which provides larger subsidies to those with

lower incomes) is more appealing for low-income workers. To the extent that employers respond to the

ACA by changing the number of workers, the composition of full versus part-time work, wages, or the use

of contracting out, this could substantially affect labor market outcomes. Finally, increases in insurance

coverage may increase the demand for labor in areas that previously had a large share uninsured in health

care occupations. 14

Our analysis focuses on labor market outcomes that could be the result of either changing supply,

changing demand, or both. Given the different channels through which the ACA-induced changes in health

insurance coverage could influence the labor market, both the sign and the magnitude of these effects are

theoretically ambiguous. It is thus ultimately an empirical question which we investigate below.

C. Prior Research on Coverage Expansions, Health Insurance and Labor Market Outcomes

An active area of prior research has examined the effect of policies that increase access to health

insurance on coverage outcomes and crowd-out, from Medicaid expansions in the 1980s and 1990s (Cutler

and Gruber 1996; Aizer and Grogger 2003; Hamersma and Kim 2013) to the 2006 Massachusetts health

14 Note that the implementation of some of these provisions has been delayed and therefore would not affect the results

in this paper. An additional provision that could affect labor market outcomes is the “Cadillac tax on high-cost plans”,

which has been delayed until 2020. This provision may cause firms to provide less generous health insurance to their

workers, which could then pass-through to higher wages. The CBO estimated that more than 80 percent of the

budgetary savings from this tax resulted from an increase in earnings rather than in direct tax revenue.

12

reform (Long et al. 2009; Yelowitz and Cannon 2010; Kolstad and Kowalski 2012; Sonier et al. 2013). An

advantage of the policies examined in these earlier papers is that they varied across states and therefore lent

themselves to quasi-experimental variation using difference-in-difference or instrumental variables

strategies to identify the impacts on coverage and other outcomes.

For several decades, health insurance in the U.S. has been tied to employment due to the exclusion

of employer-based health insurance premiums from individual income taxes. Because health insurance

outside of the employer context has historically been difficult and/or costly to obtain, economic theory

predicts that employer-sponsored health insurance could affect workers’ likelihood of being employed,

their job-to-job mobility, and decisions to retire. These predictions have spurred a large body of literature

investigating the link between health insurance and labor market outcomes. Early literature found evidence

of health insurance-induced “job lock,” or the tendency for workers to stay in jobs they would rather leave

due to concerns about losing health insurance (Madrian 1994a). Several studies that followed examined

the availability of health insurance in retirement on retirement decisions.15 While the exact estimates vary,

this literature largely finds a substantial role for post-retirement health insurance availability in explaining

retirement behavior (Gruber and Madrian, 2002).

III. EMPIRICAL METHODS

Our empirical approach leverages geographic variation in characteristics that were determined prior

to the ACA’s implementation and that influence the potential impact of different provisions of the law. We

utilize variation stemming from differences in the share of an area that is uninsured, the area’s income

distribution, and its Medicaid expansion status. For each geographic area, we calculate the pre-ACA share

of the population uninsured and with incomes less than 138% of FPL, denoting this by M*. The variable

15 Some studies took a reduced form approach, estimating the impact of employer-provided post-retirement health

insurance on retirement and found statistically significant effects of post-retirement health insurance coverage on

retirement (Madrian, 1994b; Karoly and Rogowski, 1994; Blau and Gilleskie, 2001). However, these studies often

suffered from potential bias due to the potential selection of workers with high tastes for leisure into firms that offered

post-retirement health insurance coverage. In an effort to address these concerns, Gruber and Madrian (1995) used

variation in in continuation-of-coverage regulations and found evidence that retirement behavior responds to health

insurance availability. Other studies used variation stemming from policy changes or eligibility rules for identification

(Boyle and Lahey, 2010; Fitzpatrick, 2013; Leiserson, 2013; Coe et al., 2013; Nyce et al., 2013; Shoven and Slavov,

2013). Another strand of literature took a structural approach to identify the effect of post-retirement health insurance

availability on retirement (Gustman and Steinmeier, 1994; Lumsdaine, Stock and Wise, 1996; Rust and Phelan, 1997;

Blau and Gilleskie, 2006; Blau and Gilleskie 2008; French and Jones, 2011).

13

M* represents the potential increase in Medicaid enrollment assuming all newly eligible for Medicaid enroll

in the program and no crowd out of other sources of coverage. We define E* as the increase in exchange

enrollment that would result if all who are both uninsured and between 139 and 399% of FPL acquired

coverage with no crowdout of other coverage. Both measures are averaged over the 2010-2013 period

prior to the implementation of the ACA. Note that for a region to have a high value for these factors, it

must have both a high share of individuals under 400% FPL and a high share of individuals without

insurance.

Formally, our regression equation is as follows:

(1) 𝐼𝑁𝑆𝑖𝑎𝑠𝑡 = 𝛼1 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗ + 𝛼2 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎

∗ + 𝛽𝑋𝑖𝑡 + 𝛾𝑡 + 𝜇𝑎 + ∅𝑎t + 𝜀𝑖𝑎𝑠𝑡

where 𝐼𝑁𝑆𝑖𝑎𝑠𝑡 represents an indicator for whether individual i living in area a and state s has any health

insurance, private employer coverage, privately-purchased coverage, or Medicaid coverage in time t;

𝑃𝑂𝑆𝑇𝑡 is an indicator equal to 1 in 2014 or later and 0 otherwise, 𝑀𝑎∗ and 𝐸𝑎

∗ represent the pre-ACA

measures of potential Medicaid and exchange enrollment for area a as described above, 𝑋𝑖𝑡 includes

demographic controls for gender, race, and ethnicity and age fixed effects, 𝛾𝑡 represents year fixed effects,

and 𝜇𝑎 and ∅𝑎 represent area fixed effects and area-specific time trends for area a (described in more detail

below), respectively. Both 𝑀𝑎∗ and 𝐸𝑎

∗ are demeaned, giving the coefficients on other variables the

interpretation of the effects for locations with an average level of the share uninsured within certain income

bins. Note that because we include geographic area-level fixed effects, the main effects of 𝑀𝑎∗ and 𝐸𝑎

∗ are

not included in the regression, and the main effect of 𝑃𝑂𝑆𝑇𝑡 drops out due to year fixed effects.

Our second empirical specification includes interactions between 𝑀𝑎∗ , 𝐸𝑎

∗, and 𝑃𝑂𝑆𝑇𝑡 with a binary

indicator that indicates whether the region is in a state that expanded its Medicaid program prior to the

beginning of the analysis year. In particular, we also estimate Equation (2) as follows:

(2) 𝐼𝑁𝑆𝑖𝑎𝑠𝑡 = 𝜃1 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗ + 𝜃2 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎

∗ + 𝜌0𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 + 𝜌1 𝑃𝑂𝑆𝑇𝑡 ×

𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝑀𝑎∗ + 𝜌2 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝐸𝑎

∗ + 𝛽𝑋𝑖𝑡 + 𝛾𝑡 + 𝜇𝑎 + ∅𝑎t + 𝜀𝑖𝑎𝑠𝑡

Here, 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 represents whether state s expanded Medicaid on or before January 1, 2014. In this

specification, we cluster our standard errors at the state level given that 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 varies at the state

level and to account for possible serial correlation (Bertrand et al. 2004).

14

We hypothesize that the overall level of health insurance increased after the ACA in areas with

average E* and M* and differentially more in expansion states, i.e. 𝜌0 > 0. Including measures of the area-

level income distribution in our specification allows us to explore the channels through which the ACA is

affecting health insurance coverage. We hypothesize that regions with a larger M* will have larger

increases in Medicaid coverage, but that this effect would be much more important in expansion states.

Regions with a larger E* are expected to have larger increases in privately-purchased coverage. Both M*

and E* are expected to affect overall health insurance coverage and help to identify the ACA-induced

increase in insurance in coverage.

We also hypothesize that the composition of coverage increases is different for expansion and non-

expansion states. In expansion states, Medicaid coverage is expected to increase, while Medicaid coverage

would not be expected to change by as much in non-expansion states. Since subsidies to purchase private

coverage from the exchanges are available in all states, we expect the ACA to induce higher levels of

privately-purchased insurance in both expansion and non-expansion states. The increases in privately-

purchased insurance may even be larger in non-expansion states since individuals there with incomes

between 100 and 138% of the poverty line are eligible for subsidies towards health insurance purchased on

the exchanges but not eligible for Medicaid. The impact of the ACA and Medicaid expansions on coverage

through employer-sponsored insurance is empirically ambiguous. On one hand, more employees with

access to employer coverage may sign up for health insurance through their employer due to the individual

mandate. On the other hand, there is the potential for crowd-out away from private coverage towards

Medicaid or privately-purchased health insurance given the heavily-subsidized cost.

To estimate the effect of the ACA on labor market outcomes, we perform an analogous set of

reduced-form regressions as specified in Equations (1) and (2) using labor market outcomes as the

dependent variables instead of insurance coverage. We investigate whether regions with larger ACA-

induced increases in health insurance coverage also have larger changes in labor market outcomes. The

primary channels through which any estimated effects on labor market outcomes occur can be isolated by

examining the different coefficients and their significance.

Our identification strategy isolates the impact of the ACA under the assumption that, absent the

ACA, geographic areas with larger shares of individuals uninsured and under 400% of the poverty line

15

would have evolved similarly as those with smaller shares, after controlling for fixed area-level

characteristics, area-specific time trends, and person-level demographics. In our second specification, we

assume that places with a given share of individuals both uninsured and under 400% FPL in expansion

states would have evolved similarly as those with a similar share in non-expansion states, absent the ACA.

Importantly, these assumptions do not rule out different levels of insurance coverage or labor market

outcomes across areas for reasons other than the ACA that are fixed over time (and captured by area-level

fixed effects). In addition, allowing for trends in insurance coverage and labor market outcomes to vary

across areas accounts for, for instance, differential rates of economic recovery following the recession that

could be related to the underlying income distribution in an area.

IV. DATA

For our analysis, we use data from the American Community Survey (ACS). Specifically, we use

the annual Public-Use Micro Sample (PUMS) files which contain individual- and household-level

responses that have been edited to protect the confidentiality of respondents.16 The ACS is an ongoing

household survey conducted by the U.S. Census Bureau focusing on a variety of topics including

demographic, social, and economic related questions. The ACS includes detailed information relating to

employment, demographics, health insurance coverage, and measures of poverty/income. Questionnaires

are mailed to approximately 295,000 addresses each month (or 3,540,000 annually). Follow-up phone

interviews are conducted for addresses that have not responded and personal visits are conducted by Census

field representatives to a sample of addresses that have not responded. The extensive follow up results in

a high response rate, typically around 97 percent.17

We restrict the sample to observations from 2010 through 2015 for civilians age 26 to 64, resulting

in 9,493,231 person-year observations. This sample restriction provides four years of data prior to the ACA

and two years after the ACA was implemented. We focus on ages 26 to 64 as these are the ages most likely

to be affected by Medicaid expansions and the availability of subsidies given the high rates of insurance

16 The ACS was originally developed to provide continuous information on communities across the U.S. between the

decennial Census. See http://www.census.gov/programs-surveys/acs/data/pums.html for additional details. 17 In 2013, there was a drop in the final number of housing units due to the government shutdown, as telephone and

in-person follow-up interviews were not conducted during this time.

16

coverage for the elderly and children prior to the ACA from Medicare and Medicaid, respectively, along

with other provisions of the ACA that extended coverage to dependents under age 26 starting in late 2010.

The ACS asks whether an individual has health insurance coverage at the point of interview.18 Each

individual is provided with a list of seven different types or categories of insurance coverage and can

indicate all the types of coverage through which they were insured. The choices include: insurance from

a current or former employer; insurance purchased directly from an insurance company; Medicare;

Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a

disability; Tricare or other military healthcare; VA; and Indian Health Service. It is worth noting that the

wording of these choices combined with self-reporting can lead to some measurement error in the source

of insurance coverage if, for instance, “any kind of government-assistance plan for those with low incomes”

is interpreted as subsidies that allow individuals to purchase private insurance on the exchanges.

The ACS creates a variable indicating any insurance coverage as having any of the coverage types

other than Indian Health Service. We show insurance coverage in the ACS between 2010 and 2013 for

four different age groups (0-25, 26-44, 45-64, and 65+) in Figure 4. Note that since individuals can choose

more than one type of coverage, the percentages add up to more than 100 percent. As shown in the figure,

uninsurance rates are highest for 26-64 year-olds at baseline, and the highest source of coverage for this

group is private employer coverage. The elderly are almost universally covered by Medicare, but many

have supplemental coverage from other sources. Children and young adults have relatively high rates of

coverage from Medicaid at baseline since the Medicaid eligibility criteria are generally broader for children.

Several labor market outcomes are included in the ACS. We examine whether an individual reports

being employed over the last week or out of the labor force (i.e., not employed last week and not looking

for employment over the last four weeks). The ACS also includes information about self-employment,

usual hours worked per week over the past 12 months, and wage or salary and self-employment income

over the past 12 months. We construct an indicator for part-time employment which equals one for

individuals employed last week whose hours are less than 30 per week over the past 12 months. Self-

employment is measured by an indicator variable that equals one for individuals employed last week whose

chief job activity was self-employment. We examine the effect of the ACA on hours worked conditional

18 This differs from the CPS which asks about insurance coverage in the last calendar year.

17

on being currently employed. Our baseline labor market outcomes prior to the implementation of the ACA

are summarized for different age groups in Table 1. Table 1 also includes baseline demographic

characteristics such as gender, race and ethnicity.19

Our analysis leverages geographic variation in the income distribution and the share uninsured prior

to the ACA. The finest geographic-level identified in the PUMS data are Public Use Microdata Areas, or

PUMAs.20 PUMAs are defined so as to not cross state borders and are population-based rather than based

on physical area. Each PUMA has a population of at least 100 thousand at the time of the decennial census

and the largest PUMA population in the 2010 Census is approximately 280 thousand. Large urban areas

such as Los Angeles or Chicago typically are divided into many PUMAs based on census tracts while

PUMAs in rural areas are typically based on counties.

PUMAs are redefined every ten years following the decennial Census. The ACS used 2,071

PUMAs based on the 2000 Census for years 2010 and 2011, and then switched to 2,351 PUMAs based on

the 2010 Census for years 2012 and later. In order to use data spanning this change, we use consistent

PUMAs which are an aggregation of 2000 and 2010 PUMAs with boundaries that align across decennial

Censuses.21 While this harmonization reduces the number of distinct geographic areas by approximately

half to 1,078, it does not drop any observations from the data. In addition, the aggregated PUMAs have a

larger average population (including only those aged 26 to 64) of 148,682 and some of the aggregated

PUMAs expand to cover the entire state (such as Montana). 22

Figures 5-7 show the heterogeneity in M* and E* for our sample averaged over the pre-ACA years

we examine (2010 – 2013). Figure 5 displays the variation in each measure in histograms, with each ranging

from 0 to 100. The average value of M* across PUMAs is 7.3 percent, with a standard deviation of 4.1

percent, but the share ranges from 0.3 percent to a high value of 29.0 percent. The average value of E* is

8.6 percent, ranging from a low of 0.8 percent to a high of 29.5 percent across PUMAs with a standard

deviation of 4.5 percent. Note that these shares can vary either because the share of the population within

19 Races other than Black, White or Asian are assigned as Other. A person reporting only one race is given a value of

1 for that race, and those reporting a mix of two races are credited 0.5 to each race. If a person reports more than two

races, they are assigned to Other. 20 For more information on PUMAs refer to https://www.census.gov/geo/reference/webatlas/pumas.html. 21 Consistent PUMAs are an aggregation of 2010 and 2000 PUMAs that align, within a 1% population error tolerance,

over our time period. For more information refer to: https://usa.ipums.org/usa/volii/cpuma0010.shtml. 22 For simplicity, we refer to these consistent PUMA areas as PUMAs in the remainder of the text.

18

certain income bands varies or because the share of that population that is uninsured varies. A list of

PUMAs with the highest and lowest values for M* and E* is provided in Appendix Table A.2.

Figures 6 and 7 show “heat maps” for the entire United States, California, and Los Angeles County,

with darker shaded regions representing regions with a larger value of M* and E*, respectively. Focusing

on Los Angeles County, which has the largest population of any county in the U.S., illustrates the level of

geographic detail that PUMA identifiers provide, and the significant variation present within highly-

populated counties.

While the ACS offers many advantages in sample size, response rate and survey measures, one of

the main disadvantages is that the month of interview is not available in the public use microdata for privacy

concerns. As a result, we cannot estimate how the effect of the ACA varies across the 8 quarters or 24

months of our post period, nor can we make maximum use of the timing differences in Medicaid expansion

status for dates after January 2014. However, we explore the extent to which results vary during each of the

two years following ACA implementation in the next section.

V. RESULTS

A. Health insurance coverage

We estimate Equations (1) and (2) on outcome variables pertaining to overall health insurance

coverage and its source. Table 2 displays these results. All of the specifications include controls for gender,

race, ethnicity, single year-of-age fixed effects, year fixed effects, PUMA fixed effects, and PUMA-specific

time trends. The outcome variable is listed in the column header, and the regression results are shown for

Equation (1) in the odd-numbered columns and for Equation (2) in the even-numbered columns. We

include the mean of the dependent variable in the bottom row of the table averaged over the pre-ACA

sample years for the overall sample, and separately for expansion and non-expansion states. Because the

measures of 𝑀𝑎∗ and 𝐸𝑎

∗ that we use are demeaned, the coefficient on 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 represents

the difference in coverage in expansion relative to non-expansion states after the ACA for PUMAs with

average shares of the population under 138% FPL and uninsured (7.3 percentage points) and between 139%

and 399% FPL and uninsured (8.6 percentage points).

19

The results in the table show evidence that the ACA induced statistically significant increases in

insurance coverage in both expansion and non-expansion states. In Column (1), we see that after controlling

for PUMA-level fixed effects and PUMA-specific time trends, areas with a higher share of the population

between 139% and 399% of the FPL and uninsured had higher increases in health insurance coverage:

specifically, coverage rates increased by 3.4 percentage points more in regions where E* was 10 percentage

points higher. This specification does not show evidence that coverage changes varied significantly with

the share under 138% FPL and uninsured.

When we interact 𝑃𝑂𝑆𝑇𝑡 with our measure of Medicaid expansion status and its interaction with

both E* and M* (Column (2)), we see that coverage increases were 2.3 percentage points higher in states

that expanded Medicaid relative to states that did not after controlling for demographics, year fixed effects,

PUMA fixed effects, and PUMA-specific time trends. In addition, increases in coverage are correlated

with pre-ACA population characteristics in the expected direction. In states that did not expand Medicaid,

the increase in coverage was higher in regions with a larger share of the population uninsured and between

139% and 399% FPL: a 10 percentage point increase in E* led to a 2.5 percentage point higher increase in

coverage. In states that did choose to expand Medicaid, the increase in coverage is strongly related to the

share under 138% FPL and uninsured: here, a 10 percentage point increase in M* led to a 3.9 percentage

point increase in health insurance coverage. By contrast, there is no evidence that non-expansion states had

larger coverage increases in places with a higher M*. In expansion states, the relationship between E* and

increases in health insurance coverage from the ACA is slightly smaller than that in non-expansion states,

though the difference is not statistically significant.

For both M* and E*, an increase of 10 percentage points corresponds to approximately 2.5

standard deviations. We depict the relationship between M* and E* and the change in health insurance

coverage captured in Column (2) of Table 2 graphically in Figure 8. The top panel shows scatterplots of

PUMA-level changes in health insurance coverage, condensed into 100 bins, by M* while also controlling

for E*, differentially by Medicaid expansion status. The slopes of the red dashed and blue solid lines

represent the relationship between M* and the change in health insurance coverage for expansion states and

non-expansion states, respectively. The bottom panel is similar but plots the change in coverage against

E* on the x-axis.

20

Under the assumption that our demographic controls and PUMA-level fixed effects and PUMA-

specific time trends capture the drivers of health insurance coverage aside from the ACA, we can attribute

any larger increase in health insurance coverage occurring for regions with a positive M* and E* as causally

driven by the ACA. Using the results from Column (2) to generate these ACA-driven increases at the

PUMA level and weighting by PUMA population suggests that the increases in health insurance coverage

due to the ACA were 2.6 percentage points in non-expansion states and 4.2 percentage points in expansion

states. These effects are substantial relative to the overall increase of 3.8 percentage points in non-

expansion states and 5.3 percentage points in expansion states, and represent 70 and 80 percent of the

observed increase during our study period, respectively, with the rest potentially due to improving economic

conditions.23

In Columns (3) – (8), we examine how the ACA affected source of coverage in places with high

versus low potential Medicaid and exchange enrollment. The results are similar to Columns (1) and (2),

but come from regressions in which the dependent variable is a binary indicator of whether an individual

reports having any Medicaid coverage, privately purchased coverage, and coverage from a private

employer. As described earlier, individuals surveyed by the ACS may choose multiple sources of insurance

and do not indicate which is primary. Therefore, the outcome variables represent whether an individual has

the indicated insurance at all at the time of the survey.

As shown in Column (3), when we do not distinguish between expansion and non-expansion states,

it appears that Medicaid coverage is not changing differentially in places with higher M* or E*. However,

when we interact these variables with expansion status in Column (4), we find strong evidence that

Medicaid coverage increases more in places with a higher share of the population uninsured and under

138% FPL prior to ACA. Specifically, in expansion states, in regions where M* is 10 percentage points

higher, Medicaid coverage is 5.2 percentage points higher.24

23 The overall increase in non-expansion and expansion states of 3.8 and 5.3 come from the coefficients of 𝑃𝑂𝑆𝑇𝑡 and

𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝑃𝑂𝑆𝑇𝑡 in regressions that are similar to Equation (2) but do not include year fixed effects nor M*

and E*. We also find that – as one would expect given that as shown in Figure 1 the change in coverage was not

instantaneous - the estimated effects are substantially larger in 2015 than in 2014. 24 This represents the sum of 0.0877 and 0.4318 in the table multiplied by 100.

21

We would not expect Medicaid coverage to change by as much in non-expansion states as a result

of the ACA. Consistent with this, the estimated relationship between M* and the change in Medicaid

coverage is only about one-sixth as large in states that did not expand coverage (0.09 versus 0.52). Some

uninsured individuals who were Medicaid-eligible before the ACA in non-expansion states may have

signed up due to the individual mandate or because of the additional publicity surrounding the ACA.25 The

point estimate of -0.1702 for the coefficient on 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎∗ suggests Medicaid coverage declined among

those uninsured and with incomes between 139% and 399% FPL. This suggests that, because private health

insurance became more affordable for this group, some dropped Medicaid coverage to sign up for a

subsidized private plan through their state exchange.26

While the coefficients on 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗ and 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎

∗ in Column (4) are both statistically

significant, they have opposite signs, and in specifications that include the sum of M* and E*, their

coefficient is economically small and negative (see Appendix Table A.3). This suggests that the ACA

induced very little increase in Medicaid coverage in states that did not expand the program. Performing an

exercise similar to that above, we find that the ACA increased Medicaid coverage by 2.1 percentage points

(40 percent of the total increase in health insurance coverage) in expansion states and by a negligible amount

in non-expansion states.27

Columns (5) and (6) show that while the increase in privately-purchased health insurance was also

substantial overall, the average effect is significantly smaller in magnitude in states that expanded their

Medicaid programs. In non-expansion states, the increase is strongly correlated with the share of the

population between 139% and 399% FPL and uninsured who are most likely to purchase subsidized

insurance from the exchanges. Our simulations suggest that the ACA increased privately-purchased

insurance by 2.4 percentage points (or 63 percent of the overall increase) in non-expansion states but by

just 1.0 percentage points (or 19 percent of the overall increase) in expansion states. The statistically

25 This is referred to as the “woodwork effect”. Frean, Gruber, and Sommers (2017) find the woodwork effect

accounted for 30% of the ACA’s impact on coverage and similar spillovers have also been documented in earlier

expansions (Aizer and Grogger, 2003; Dubay and Kenny, 2003). Other estimates, just prior to the ACA’s

implementation, also suggest the large potential magnitude of the woodwork effect in the ACA (Sommers and

Epstein, 2011; Sonier et. al. 2013). 26 According to individual-level data from the 2010-2013 ACS, 41 percent of those aged 26 to 64 and enrolled in

Medicaid had family incomes above 138 percent of the federal poverty line. 27 Specifically, we use the results from Column (2) to generate ACA-driven increases in Medicaid coverage at the

PUMA level and weight by the population age 26-64 in each PUMA.

22

significant estimate of -0.0826 in column (6) for the 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗ coefficient is comparable in magnitude

but opposite in sign to the corresponding estimate for Medicaid coverage in Column (4). This suggests that

– even in states that did not expand Medicaid – greater awareness of this coverage caused some low-income

individuals to shift from private to public coverage.

Finally, Columns (7) and (8) show little evidence that health insurance coverage from private

employers changed substantially in either direction as a result of the ACA, suggesting that there was little

crowd-out occurring. Figures similar to Figure 8 but for Medicaid coverage, privately-purchased coverage

and coverage from private employers are included in the appendix.

We examine the heterogeneity in the effects of the ACA on health insurance coverage by minority

status, presence of a dependent child, education, sex, age, and marital status in Table 3. In these

specifications, we retain the definitions of M* and E* used above, so that these two variables represent the

averages for the overall adult population rather than for the sub-populations considered here. Estimates for

the 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝑃𝑂𝑆𝑇𝑡 coefficients suggest a relatively large average gain in health insurance

coverage in states that expanded Medicaid for childless adults, for adults with only a high school degree or

less, and for unmarried adults. This is consistent with the prediction that the Medicaid expansion would

differentially affect coverage for these groups. And consistent with the results in the previous table, the

estimates for the 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎∗ and 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎

∗ coefficients are positive and, in 20 out of

24 cases, statistically significant. In contrast, only 1 of 24 estimates for the 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗ and the

𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎∗ coefficients are significant.

Taken together, these results reveal that the ACA induced significantly larger increases in coverage

in low-income areas in expansion states than in states that did not expand. Additionally, coverage in areas

with a large share uninsured and with incomes between 139% and 399% FPL rose significantly in both

groups of states. Even after controlling for the share uninsured, increases in coverage were much greater in

states that expanded their Medicaid programs.

In Appendix Table A.4, we estimate a companion set of specifications in which we sum E* and M*

(rather than including each separately). For 10 of the 12 sub-groups that we consider, the increase in

coverage is significantly greater in areas with high values of E* + M* in states that did not expand their

Medicaid programs. Furthermore, this relationship is significantly stronger for all 12 sub-groups in states

23

that did expand their Medicaid programs. And finally, for all twelve sub-groups, the increases in coverage

are significantly greater in states that expanded even after controlling for E* + M*.

B. Labor market outcomes

We provide evidence on the effect of the ACA on labor market outcomes in Tables 4 and 5, which

display the results of estimating Equations (1) and (2) on the labor market outcomes summarized in Table

1. Table 4 sheds light on how the ACA affected labor market outcomes on the extensive margin (e.g. labor

force participation), and Table 5 focuses on labor market outcomes on the intensive margin (e.g. hours of

work). As in earlier tables, the column heading denotes the dependent variable. All of the results reported

are from regressions that include basic demographic controls, year fixed effects, PUMA fixed effects and

PUMA-specific time trends. Pre-ACA means of the dependent variables are reported in the final three rows

below the regression results.

If the ACA-induced changes in health insurance coverage were influencing labor market outcomes,

one would expect to detect a significant coefficient on 𝑃𝑂𝑆𝑇𝑡 × 𝑀𝑎∗, 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑎

∗ or their interactions with

the state’s Medicaid expansion decision. As the first and second columns show, the change in the share of

adults aged 26 to 64 out of the labor force is significantly related with both M* and E* but in opposing

directions. Specifically, the results indicate that there is an increase in labor force participation in areas

where the share uninsured and under 138% FPL is larger and a reduction in labor force participation in

areas where the share uninsured and between 139% and 399% FPL is larger. The latter effect may appear

surprising given that dropping out of the labor force and earning no income would make one ineligible for

subsidies through the ACA, which are not available to those with income below 100% FPL. However,

since subsidies are based on total household income, one member could drop out of the labor force while

another continued to work, thus increasing the family’s total subsidy.

As discussed earlier, the increase in labor force participation among those in areas with a greater

proportion of individuals uninsured and under 138% FPL could occur if, for instance, people prefer health

insurance policies offered on the exchanges to Medicaid coverage (in expansion states) or people are

induced to work more in order to become eligible for exchange subsidies (in non-expansion states). The

results in Column (2) provide no evidence that this effect differs in expansion and non-expansion states,

24

suggesting that both of these mechanisms may be at play. As mentioned earlier, the effects of Medicaid

expansions as part of the ACA may be different than in isolation because a person can obtain subsidized

private health insurance coverage even if their earnings rise above the Medicaid eligibility threshold. An

additional possible margin is that the expansions in health insurance coverage in relatively poor areas may

increase labor demand and thus total employment in the health care sector.28

The subsequent columns in Table 4 provide additional information on the source of the changes in

labor force participation. Columns (3) and (4) display the results of estimating Equations (1) and (2) on

employment and Columns (5) and (6) provide evidence on the ACA’s effects on unemployment. The

results indicate that the increased labor force participation in areas where M* is high reflects an increase in

employment, and are suggestive that the reduction in labor force participation in areas where E* is high is

roughly equally split between a reduction in employment and a reduction in unemployment, though we do

not have sufficient power to decompose the source definitively. Unemployment could fall if, for example,

workers became less likely to search for a job and more likely to drop out of the labor force because of the

availability of subsidized health insurance.29

Table 5 presents results on the effects of the ACA on part-time employment, self-employment and

hours worked (conditional on working). We find little evidence to suggest that these outcomes changed

differentially in places with higher potential Medicaid or exchange enrollment. The only estimate in the

table that is statistically significant is the estimated coefficient of 𝑃𝑂𝑆𝑇𝑡 × 𝐸𝑋𝑃𝐴𝑁𝑆𝐼𝑂𝑁𝑠 × 𝐸𝑎∗ in Column

(6), providing weak evidence that subsidy-induced changes in hours are different in expansion and non-

expansion states. However, the total relationship between reported hours conditional on working and areas

with higher potential exchange enrollment in expansion states is still economically negligible and

statistically insignificant.

In Table 6, we report the results of specifications that aggregate M* and E* into E* + M* rather

than distinguishing these two potential channels using our extensive margin measures of labor market

outcomes. The statistically insignificant point estimate of 0.0054 in Column (2) suggests that labor force

28 Consistent with this, Chodorow-Reich et al (2012) find that increases in Medicaid funding from the American

Recovery and Reinvestment Act increased employment in the health care sector. 29 Autor and Duggan (2003) find that an increase in the generosity of the SSDI program reduced unemployment

because unemployed workers became more likely to drop out of the labor force rather than search for a new job.

25

participation did not change differentially in areas with a large share both uninsured and with family

incomes below 400% FPL. Multiplying this point estimate by the average value for E* + M* would suggest

a change in labor force participation of just 0.003 percentage points. These results highlight that failing to

distinguish between areas with different pre-ACA income distributions would mask the heterogeneous

effects that we estimate on labor force participation.

The next four columns summarize specifications for employment and unemployment. The results

reported there suggest that areas with a high share uninsured and below 400% FPL experienced significant

increases in employment and reductions in unemployment. These two effects roughly balance out so that

there is no corresponding change in labor force participation. Additionally, states that expanded their

Medicaid programs experienced larger average reductions in unemployment. In addition to the labor supply

margins highlighted above, the policy-induced increase in insurance coverage may have increased labor

demand and thus the total number employed.

We estimate Equation (2) for each of the subsamples defined earlier in Table 3 with labor force

non-participation as the outcome variable and display the results in Table 7. The results reveal that – for

none of the 12 sub-populations we consider – did labor force participation change differentially in states

that expanded their Medicaid programs. We do, however, find a stronger relationship between labor force

participation changes and area-level uninsurance rates for minorities, childless adults, men, and married

individuals. Minorities, childless adults, and men were more likely to be uninsured pre-ACA and those

who are married may be more responsive to labor supply incentives. For each group, labor force

participation rose significantly in areas with a large share both poor and uninsured while it fell significantly

in areas with a large share with middle incomes and uninsured.

Theoretically, one might expect a larger average labor supply response by older workers given that

the average disutility of work likely rises with age and because more of these workers may be on the margin

for retirement. We therefore also examine how the estimated effects of the ACA-induced increases in

insurance coverage vary by age group. While the results in Columns (9) and (10) of Table 7 are not

statistically significant, the point estimates are similar to those that are estimated in the full sample,

suggesting that we lose precision when cutting the sample by age and that effects are not very different

between 26-44 and 45-64 year olds.

26

In Appendix Table A.5, we estimate a companion set of specifications in which we sum E* and

M* (rather than including each separately). This table shows 36 coefficient estimates, with three for each

of the twelve sub-groups. An examination of this table reveals that just 2 of the 36 coefficient estimates are

significant at the 5 percent level, suggesting very little average effect. In contrast, 34 of the 36 estimates in

the corresponding table for health insurance coverage were statistically significant.

Taken together, our findings demonstrate that the aggregate labor market effects are relatively small

in both expansion and non-expansion states. However, this aggregate impact masks countervailing effects

through two channels: reductions in labor force participation coming from the availability of subsidies to

purchase insurance through new health insurance exchanges, and increases in labor force participation

stemming from a combination of incentives to enter the labor force and become eligible for subsidized

coverage through the health insurance exchanges and preferences for exchange policies over public

insurance options. We note that our empirical strategy does not allow us to distinguish between income

effects and incentive effects in that some of the effects we estimate could be due to larger government

transfers resulting in a higher demand for leisure. The increase in insurance coverage and the associated

rise in health care spending may also influence labor market outcomes through a rise in labor demand.

C. Pre-trends, year-by-year results, and robustness

Our identification rests on the assumption that, absent the ACA, geographic areas with larger shares

of uninsured individuals under 400% FPL would have evolved similarly as those with smaller shares, after

controlling for person-level demographics, year effects, area-level fixed effects, and area-specific time

trends. We further assume that places with a given share of individuals both uninsured and under 400%

FPL in expansion states would have evolved similarly as those with a similar share in non-expansion states,

absent the ACA. Both assumptions may be violated if we see areas with high levels of potential Medicaid

or exchange enrollment trending differently before ACA implementation.

We investigate trends in the relationship between M*, E* and health insurance by estimating

versions of Equation (2) where we replace 𝑃𝑂𝑆𝑇𝑡 with a full set of year dummy variables to trace out the

relationship between health insurance coverage and potential Medicaid and exchange enrollment before

and after the ACA took effect. Because our data do not permit analysis on a quarterly or monthly basis, the

27

estimates represent the effects on average throughout the year. Because we include PUMA-specific time

trends, two coefficients on year drop out of the equation.

The results of running fully-interacted specifications with health insurance coverage as the outcome

are provided in Figure 9. The effects mirror those shown in Table 2 but also show that the relationship

between potential Medicaid and exchange enrollment and health insurance coverage was generally stable

and statistically indistinguishable from zero in the pre-ACA period, while increasing markedly in the post-

ACA period. The main exception is that health insurance coverage is not related to M* in non-expansion

states post-ACA, which is consistent with our prior findings and intuitive. We include similar figures for

the different sources of coverage, namely Medicaid, privately-purchased insurance and coverage from

private employers, in the appendix.

Finally, we also examine the robustness of our results to different specifications and definitions of

geographic areas. As mentioned previously, our analysis uses aggregated PUMAs in order to include years

prior to the change in PUMA definition, which allows a more systematic analysis of pre-trends that could

otherwise confound our results. However, using these aggregated PUMAs results in approximately half

the number of geographic areas and coarser variation than would be present otherwise. We examine the

robustness of our results to using the finer set of PUMAs, limiting the sample years to 2012-2015.30 These

results are consistent with our results using the full sample period and the aggregated PUMAs and provide

additional evidence that the ACA led to increases in health insurance coverage with little evidence that

labor market outcomes changed due to the ACA’s provisions. Finally, we also examine the sensitivity of

including states that expanded their Medicaid programs during 2014 as expansion states in our analysis and

find that our results are qualitatively and quantitatively similar. Both of these sets of results are available

upon request.

VI. CONCLUSION

The Affordable Care Act represents the most significant change to the U.S. health care system and

implemented a variety of policies that sought to reduce the number uninsured while simultaneously slowing

the growth rate of health care spending. Our paper shows that a large share (75 percent) of the drop in

30 Due to the limited number of years, we omit PUMA-specific time trends from these specifications.

28

uninsurance that occurred after the key components of the ACA were implemented was due to these

provisions and occurred differentially in regions where a greater share of the population was uninsured and

eligible for Medicaid or for private insurance subsidies.

Due to the strong link between health insurance coverage and employment, the CBO predicted a

sizable reduction in the labor force as a result of the ACA. Many proponents of the legislation predicted

positive employment effects from improved health and subsidies for small businesses (CEA, 2009). If the

ACA had led to a reduction (or increase) in employment, hours, or labor force participation relative to what

it would have been in the absence of the ACA, we would have expected to find evidence of larger changes

in labor market outcomes in regions that were differentially affected by the legislation or among

subpopulations with higher labor supply elasticities.

Data from the first two years of the law’s implementation suggests that, in the aggregate, any labor

supply effects of the ACA have been modest. This is consistent with the time series evidence in Figure 2,

which shows no evidence of a change in the level or trend of labor force participation after ACA

implementation. However, this aggregate effect masks countervailing effects in regions with higher

potential Medicaid enrollment, where labor force participation increased, and regions with higher potential

exchange enrollment, where labor force participation decreased. More work is clearly needed to estimate

the economic effects of this legislation as additional years of data become available.

29

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34

Figure 1

Percentage of Persons 18-64 Uninsured, January 2010 – March 2016

Source: NCHS. National Health Interview Survey Early Release of Quarterly Estimates, retrieved September 12,

2016.

Figure 2

Labor Force Participation Rate, Seasonally Adjusted, Ages 16 and over

Source: Bureau of Labor Statistics, Series LNS11300000, retrieved September 12, 2016.

10

12

14

16

18

20

22

24

60

61

62

63

64

65

66

67

20

08

Jan

20

08

Au

g

20

09

Mar

20

09

Oct

20

10

May

20

10

Dec

20

11

Ju

l

20

12

Feb

20

12

Sep

20

13

Ap

r

20

13

No

v

20

14

Ju

n

20

15

Jan

20

15

Au

g

20

16

Mar

Lab

or

Forc

e Pa

rtic

ipat

ion

Rat

eACA Takes Effect

35

Figure 3

Notes: Data on each state’s decision to expand or not expand their Medicaid program was retrieved from Kaiser

Family Foundation, Status of State Action on the Medicaid Expansion Decision, on August 31, 2016.

Figure 4

36

Figure 5

Heterogeneity across PUMAs in M* and E*

(a) Share < 138% FPL and Uninsured in 2010-13

(b) Share 139-399% FPL and Uninsured in 2010-13

Notes: M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13. E*

refers to the share of individuals in a PUMA that are uninsured and within 139-399% FPL in 2010-13.

Average = 7.31

Average = 8.60

37

Figure 6

M* in PUMAs across United States, California and Los Angeles County

Notes: M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13.

38

Figure 7

E* in PUMAs across United States, California and Los Angeles County

Notes: E* refers to the share of individuals in a PUMA that are uninsured and within 139-399% FPL in 2010-13.

39

Figure 8

Change in Health Insurance Coverage by M*, E*, and Medicaid Expansion Status

(a) M*

(b) E*

Notes: M* refers to the share of individuals in a PUMA that are uninsured and 0-138% FPL in 2010-13 and E* refers

to the share of in a PUMA that are uninsured and 139-399% FPL in 2010-13.

40

Figure 9

Relationship between M*, E* and Health Insurance Coverage by Year and Medicaid

Expansion Status

(a) M*

(b) E*

Notes: M* refers to the share of individuals in a PUMA that are uninsured and 0-138% FPL in 2010-13 and E*

refers to the share of in a PUMA that are uninsured and 139-399% FPL in 2010-13.

41

Table 1:

Baseline Demographics and Labor Market Outcomes

Ages

26-34 35-44 45-54 55-64

# of Observations (Unweighted) 1,280,749 1,497,595 1,818,968 1,729,822

Female 50% 50% 51% 52%

White 72% 73% 77% 81%

Black 13% 13% 12% 11%

Asian 7% 7% 5% 4%

Other 8% 7% 5% 4%

Hispanic 20% 19% 13% 9%

% of FPL 287% 309% 336% 344%

NILF 18% 18% 20% 36%

Employed 74% 76% 74% 60%

Self-Employed 4% 7% 9% 8%

Part-Time 8% 7% 7% 7%

Hours 40 41 41 40

Single Coverage 68% 72% 74% 74%

Double Coverage 4% 6% 7% 11%

# of Health Insurance Plans 0.78 0.85 0.92 1.02

Notes: Baseline summary statistics for years 2010-2013. ACS survey weights used.

42

Table 2:

Effect of Potential Medicaid/Exchange Enrollment and Medicaid Expansion on Overall Health Insurance Coverage

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES

Health

Insurance

Coverage

Health

Insurance

Coverage

Medicaid

Coverage

Medicaid

Coverage

Private

Purchase

Private

Purchase

Private

Employer

Private

Employer

Post x M* 0.0361 0.0244 0.0536 0.0877** -0.0107 -0.0826* 0.0280 0.0611

(0.0926) (0.0718) (0.1013) (0.0426) (0.0264) (0.0442) (0.0476) (0.0911)

Post x E* 0.3389*** 0.2463** 0.0061 -0.1702*** 0.1949** 0.3260** 0.1070** 0.0707

(0.1100) (0.0956) (0.1617) (0.0568) (0.0738) (0.1333) (0.0532) (0.0873)

Expansion x Post 2.2653*** 3.3507*** -0.4964** -0.2180

(0.2879) (0.2918) (0.2192) (0.1913)

Expansion x Post x M* 0.3709** 0.4318*** 0.0775 -0.1043

(0.1395) (0.1518) (0.0547) (0.1021)

Expansion x Post x E* -0.0589 0.0040 -0.2045 0.0953

(0.1403) (0.1436) (0.1391) (0.1058)

Observations 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231

R-squared 1.0000 1.0000 0.9993 0.9993 0.9991 0.9991 0.9999 0.9999

Pre-ACA Dependent Var Mean 79.88 79.88 10.53 10.53 9.31 9.31 60.75 60.75

Non-Expansion States 77.98 77.98 9.20 9.20 9.32 9.32 59.54 59.54

Expansion States 81.74 81.74 11.83 11.83 9.31 9.31 61.94 61.94

Notes: M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13 and E* refers to the share of individuals in a PUMA

that are uninsured and within 139-399% FPL in 2010-13. All shares M* and E* are standardized to have a mean of 0. Sample includes civilians ages 26-64 in the

years 2010-2015. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, ** p<0.05, * p<0.1). Controls include demographic controls

(female, black, white, asian, other, hispanic), year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

43

Table 3: Heterogeneity in Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Health Insurance Coverage by Observable Characteristics

(1) (2) (3) (4) (5) (6)

VARIABLES Non-minority Minority

Childless

Adult

Has Child

Under 18 HSG or less

Some College

or More

Post x M* 0.0533 -0.0634 0.0270 0.0002 -0.0455 0.0626

(0.0961) (0.1034) (0.0862) (0.1217) (0.1080) (0.0579)

Post x E* 0.1143 0.2896 0.2093 0.3451*** 0.2911** 0.1990**

(0.0880) (0.1763) (0.1338) (0.1206) (0.1287) (0.0912)

Expansion x Post 2.1164*** 2.1588*** 2.5621*** 1.6543*** 3.3077*** 1.4321***

(0.2952) (0.6043) (0.3314) (0.4012) (0.5775) (0.2280)

Expansion x Post x M* 0.4443** 0.4127** 0.2985* 0.5100*** 0.4288* 0.2684***

(0.1932) (0.1604) (0.1537) (0.1875) (0.2266) (0.0958)

Expansion X Post x E* -0.0682 -0.1551 0.0886 -0.3671** -0.1472 -0.0060

(0.1716) (0.1943) (0.1745) (0.1816) (0.2077) (0.1145)

Observations 6,537,661 2,955,570 6,266,853 3,226,378 3,602,122 5,891,109

R-squared 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Pre-ACA DV Mean 85.56 69.39 78.07 83.19 68.73 87.03

Non-Exp States 83.85 65.85 76.43 80.82 66.79 85.69

Expansion States 87.35 72.39 79.68 85.50 70.80 88.26

(7) (8) (9) (10) (11) (12)

VARIABLES Male Female

Young

(26-44)

Near Elderly

(45-64) Married Not Married

Post x M* 0.0118 0.0358 0.0393 0.0118 -0.0099 0.0103

(0.0841) (0.0911) (0.0883) (0.0756) (0.0636) (0.1056)

Post x E* 0.2743*** 0.2194* 0.1800* 0.3042** 0.3732*** 0.1015

(0.0921) (0.1262) (0.0973) (0.1158) (0.0978) (0.1229)

Expansion x Post 2.4995*** 2.0416*** 2.4688*** 2.0734*** 1.6641*** 2.8224***

(0.3116) (0.3289) (0.3922) (0.2483) (0.2761) (0.5191)

Expansion x Post x M* 0.2745* 0.4696*** 0.3695** 0.3645** 0.3501*** 0.3649*

(0.1391) (0.1719) (0.1629) (0.1405) (0.1110) (0.2097)

Expansion X Post x E* 0.0368 -0.1567 -0.0650 -0.0466 -0.1410 0.0459

(0.1381) (0.1739) (0.1512) (0.1636) (0.1344) (0.2016)

Observations 4,629,672 4,863,559 4,167,626 5,325,605 5,875,157 3,618,074

R-squared 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Pre-ACA DV Mean 77.53 82.16 75.25 84.26 86.02 71.17

Non-Exp States 75.75 80.13 72.79 82.82 84.75 68.16

Expansion States 79.25 84.16 77.61 85.68 87.29 74.02

Notes: Dependent variable is health insurance coverage and each column corresponds to a separate subsample. “Non-

minority” indicates white non-Hispanic. “Has Child Under 18” refers to individuals with at least one child living in

the same household who is under 18. “HSG or less” indicates that a high-school degree (including GED) or less is the

highest educational attainment. “Male” refers to gender. “Young” are ages 26-44. “Married” refers to marital status.

M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13 and E* refers

to the share of individuals in a PUMA that are uninsured and within 139-399% FPL in 2010-13. All shares M* and

E* are standardized to have a mean of 0. Robust standard errors are in parentheses and are clustered by state (***

p<0.01, ** p<0.05, * p<0.1). Controls include demographic controls (female, black, white, asian, other, hispanic),

year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

44

Table 4: Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Labor Market Outcomes: Extensive Margin

(1) (2) (3) (4) (5) (6)

VARIABLES

Not in the

Labor Force

Not in the

Labor Force Employed Employed Unemployed Unemployed

Post x M* -0.0847*** -0.0801** 0.0773** 0.0787** 0.0074 0.0014

(0.0298) (0.0399) (0.0369) (0.0344) (0.0225) (0.0225)

Post x E* 0.0962*** 0.1064** -0.0535 -0.0634 -0.0427** -0.0431

(0.0343) (0.0448) (0.0342) (0.0383) (0.0174) (0.0261)

Expansion x Post 0.0616 0.2182 -0.2798*

(0.1502) (0.1488) (0.1415)

Expansion x Post x M* -0.0044 0.0297 -0.0253

(0.0684) (0.0799) (0.0487)

Expansion X Post x E* -0.0191 -0.0028 0.0218

(0.0734) (0.0721) (0.0409)

Observations 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231

R-squared 0.9997 0.9997 1.0000 1.0000 0.9984 0.9984

Pre-ACA Dependent Var Mean 22.56 22.56 71.21 71.21 6.23 6.23

Non-Expansion States 23.33 23.33 70.70 70.70 5.97 5.97

Expansion States 21.81 21.81 71.71 71.71 6.48 6.48

Notes: M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13 and E* refers to the share of individuals in a PUMA

that are uninsured and within 139-399% FPL in 2010-13. All shares M* and E* are demeaned to have a mean of 0. Sample includes civilians ages 26-64 in the

years 2010-2015. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, ** p<0.05, * p<0.1). Controls include demographic controls

(female, black, white, asian, other, hispanic), year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

45

Table 5: Effect of Potential Medicaid/Exchange Enrollment and Medicaid Expansion on Labor Market Outcomes: Intensive

Margin

(1) (2) (3) (4) (5) (6)

VARIABLES Part-Time Part-Time

Self-

Employed

Self-

Employed Hours Hours

Post x M* 0.0363 0.0321 0.0078 -0.0023 0.0013 0.0143

(0.0238) (0.0351) (0.0221) (0.0289) (0.0117) (0.0171)

Post x E* -0.0375 -0.0085 -0.0122 0.0052 -0.0030 -0.0332

(0.0261) (0.0384) (0.0302) (0.0421) (0.0110) (0.0223)

Expansion x Post -0.0984 -0.0399 0.0575

(0.0926) (0.0765) (0.0462)

Expansion x Post x M* -0.0094 0.0154 -0.0168

(0.0496) (0.0442) (0.0217)

Expansion X Post x E* -0.0397 -0.0297 0.0496*

(0.0511) (0.0598) (0.0267)

Observations 9,493,231 9,493,231 9,493,231 9,493,231 6,706,941 6,706,941

R-squared 0.9988 0.9988 0.9988 0.9988 1.0000 1.0000

Pre-ACA Dependent Var Mean 7.19 7.19 7.26 7.26 40.65 40.65

Non-Expansion States 6.73 6.73 7.08 7.08 40.97 40.97

Expansion States 7.64 7.64 7.44 7.44 40.34 40.34

Notes: M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13 and E* refers to the share of individuals in a PUMA

that are uninsured and within 139-399% FPL in 2010-13. All shares M* and E* are demeaned to have a mean of 0. Sample includes civilians ages 26-64 in the

years 2010-2015. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, ** p<0.05, * p<0.1). Controls include demographic controls

(female, black, white, asian, other, hispanic), year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

46

Table 6: Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Labor Market Outcomes: Pooled Income Groups

(1) (2) (3) (4) (5) (6)

VARIABLES

Not in the

Labor Force

Not in the

Labor Force Employed Employed Unemployed Unemployed

Post x (E*+M*) 0.0001 0.0054 0.0160* 0.0136 -0.0161* -0.0190**

(0.0068) (0.0090) (0.0094) (0.0082) (0.0082) (0.0085)

Expansion x Post 0.1311 0.1556 -0.2867**

(0.1413) (0.1385) (0.1379)

Expansion x Post x (E*+M*) -0.0057 0.0093 -0.0036

(0.0128) (0.0149) (0.0133)

Observations 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231

R-squared 0.9997 0.9997 1.0000 1.0000 0.9984 0.9984

Pre-ACA Dependent Var Mean 22.56 22.56 71.21 71.21 6.23 6.23

Non-Expansion States 23.33 23.33 70.70 70.70 5.97 5.97

Expansion States 21.81 21.81 71.71 71.71 6.48 6.48

Notes: (E*+M*) refers to the share of individuals in a PUMA that are uninsured and 0-399% FPL in 2010-13. All shares are standardized to have a mean of 0.

Sample includes civilians ages 26-64 in the years 2010-2015. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, ** p<0.05, * p<0.1).

Controls include demographic controls (female, black, white, asian, other, hispanic), year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

47

Table 7: Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Not in the Labor Force by Observable Characteristics

(1) (2) (3) (4) (5) (6)

VARIABLES Non-minority Minority

Childless

Adult

Has Child

Under 18 HSG or less

Some College

or More

Post x M* -0.0290 -0.1268** -0.1312*** 0.0254 -0.0648 -0.1184**

(0.0481) (0.0630) (0.0470) (0.0553) (0.0629) (0.0529)

Post x E* 0.0308 0.1924** 0.1355*** 0.0253 0.1174 0.1337**

(0.0414) (0.0797) (0.0462) (0.0715) (0.0819) (0.0635)

Expansion x Post 0.0657 0.0926 0.1379 -0.0999 0.1825 0.0182

(0.2041) (0.2104) (0.1851) (0.1918) (0.2382) (0.1649)

Expansion x Post x M* -0.0590 0.0450 0.0105 -0.0396 -0.0516 0.0461

(0.0981) (0.0787) (0.0864) (0.0940) (0.0854) (0.0921)

Expansion X Post x E* 0.0523 -0.0943 -0.0264 0.0223 0.0331 -0.0989

(0.1058) (0.0946) (0.0863) (0.1112) (0.1011) (0.0950)

Observations 6,537,661 2,955,570 6,266,853 3,226,378 3,602,122 5,891,109

R-squared 0.9996 0.9998 0.9997 0.9996 0.9998 0.9996

Pre-ACA DV Mean 21.58 24.38 25.39 17.38 30.60 17.41

Non-Exp States 22.34 25.39 26.61 17.32 31.34 17.82

Expansion States 20.78 23.52 24.21 17.43 29.82 17.03

(7) (8) (9) (10) (11) (12)

VARIABLES Male Female

Young

(26-44)

Near Elderly

(45-64) Married Not Married

Post x M* -0.1684*** 0.0014 -0.0910 -0.0669 -0.0954** -0.0369

(0.0565) (0.0875) (0.0797) (0.0456) (0.0450) (0.0648)

Post x E* 0.2211*** -0.0043 0.1336 0.0781 0.1426*** 0.0342

(0.0607) (0.0908) (0.1038) (0.0551) (0.0512) (0.0648)

Expansion x Post -0.1475 0.2723 0.0320 0.0801 -0.0047 0.1475

(0.2045) (0.1918) (0.2006) (0.2076) (0.1553) (0.2262)

Expansion x Post x M* 0.0844 -0.0875 0.0837 -0.0942 0.0152 -0.0353

(0.0842) (0.1059) (0.1041) (0.1181) (0.0862) (0.0921)

Expansion X Post x E* -0.1468* 0.1052 -0.1264 0.0945 -0.0680 0.0551

(0.0871) (0.1131) (0.1326) (0.1133) (0.0808) (0.1035)

Observations 4,629,672 4,863,559 4,167,626 5,325,605 5,875,157 3,618,074

R-squared 0.9996 0.9998 0.9997 0.9997 0.9996 0.9998

Pre-ACA DV Mean 17.14 27.82 17.74 27.11 21.23 24.45

Non-Exp States 17.94 28.53 18.12 28.19 21.77 25.60

Expansion States 16.35 27.12 17.37 26.05 20.70 23.35

Notes: Dependent variable is not in the labor force and each column corresponds to a separate subsample. “Non-

minority” indicates white non-Hispanic. “Has Child Under 18” refers to individuals with at least one child living in

the same household who is under 18. “HSG or less” indicates that a high-school degree (including GED) or less is the

highest educational attainment. “Male” refers to gender. “Young” are ages 26-44. “Married” refers to marital status.

M* refers to the share of individuals in a PUMA that are uninsured and within 0-138% FPL in 2010-13 and E* refers

to the share of individuals in a PUMA that are uninsured and within 139-399% FPL in 2010-13. All shares M* and

E* are standardized to have a mean of 0. Robust standard errors are in parentheses and are clustered by state (***

p<0.01, ** p<0.05, * p<0.1). Controls include demographic controls (female, black, white, asian, other, hispanic),

year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

48

Appendix A: Additional Tables and Figures

Figure A.1

Figure A.2

49

Figure A.3

Change in Medicaid Coverage by M*, E*, and Medicaid Expansion Status

50

Figure A.4

Change in Privately-Purchased Coverage by M*, E*, and Medicaid Expansion Status

51

Figure A.5

Change in Private Employer Coverage by M*, E*, and Medicaid Expansion Status

52

Figure A.6

Relationship between M*, E* and Medicaid Coverage by Year and Medicaid Expansion Status

53

Figure A.7

Relationship between M*, E* and Privately-Purchased Coverage by Year and Medicaid

Expansion Status

54

Figure A.8

Relationship between M*, E* and Private Employer Coverage by Year and Medicaid

Expansion Status

55

Table A.1

Source: Kafka (2015)

56

Table A.2

PUMAs with Lowest and Highest Values of M* and E*

Lowest Values of M*

Middlesex (Southeast) & Norfolk (Northeast) Counties--Newton City & Brookline Town

Norfolk County (Northeast)--Quincy City & Milton Town

Northwest Vermont--Chittenden, Franklin & Grand Isle Counties--Burlington City

Honolulu County--Moanalua to Pearl City

Waukesha County (West)

Lowest Values of E*

Middlesex (Southeast) & Norfolk (Northeast) Counties--Newton City & Brookline Town

District of Columbia (West)

Honolulu County--Moanalua to Pearl City

Montgomery County (South)--Bethesda, Potomac & North Bethesda

Howard County (West)--Columbia (West) & Ellicott City (Northwest)

Highest Values of M*

Hidalgo County--Alamo, Donna, McAllen, Mission, Pharr, San Juan, Hidalgo, Weslaco, Mercedes,

Edinburg City

Los Angeles County (Central and South Central)--LA City (Southeast/East Vernon, South

Central/Westmont, South Central/Watts)

Cameron County--Brownsville, Harlingen, San Benito Cities

Los Angeles County (Central)--LA City (Central/Koreatown)

South Texas Development Council--Webb County (North & East)--Laredo City

Highest Values of E*

Los Angeles County (Central)--LA City (Central/Koreatown)

Los Angeles County (Central)--East Los Angeles

Los Angeles County--LA City (Central/Univ. of Southern California & Exposition Park)

Prince George's County (Northwest)--College Park City & Langley Park

Los Angeles County (South and Central)--Bell Gardens, Bell, Maywood, Cudahy, Commerce,

Huntington Park, Florence-Graham, Walnut Park, South Gate, Lynwood City

57

Table A.3: Heterogeneity in Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Health Insurance Coverage by Observable Characteristics: Pooled Income Groups

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES

Health

Insurance

Coverage

Health

Insurance

Coverage

Medicaid

Coverage

Medicaid

Coverage

Private

Purchase

Private

Purchase

Private

Employer

Private

Employer

Post x (E*+M*) 0.1781*** 0.1261*** 0.0314 -0.0305** 0.0857*** 0.1047** 0.0650*** 0.0655***

(0.0433) (0.0407) (0.0492) (0.0121) (0.0276) (0.0515) (0.0109) (0.0190)

Expansion x Post 2.2570*** 3.1557*** -0.3992* -0.1702

(0.2967) (0.3167) (0.2120) (0.1761)

Expansion x Post x (E*+M*) 0.1673*** 0.2140*** -0.0478 -0.0062

(0.0466) (0.0244) (0.0527) (0.0244)

Observations 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231 9,493,231

R-squared 1.0000 1.0000 0.9993 0.9993 0.9991 0.9991 0.9999 0.9999

Pre-ACA Dependent Var Mean 79.88 79.88 10.53 10.53 9.31 9.31 60.75 60.75

Non-Expansion States 77.98 77.98 9.20 9.20 9.32 9.32 59.54 59.54

Expansion States 81.74 81.74 11.83 11.83 9.31 9.31 61.94 61.94

Notes: (E*+M*) refers to the share of individuals in a PUMA that are uninsured and within 0-399% FPL in 2010-13. All shares are standardized to have a mean

of 0. Sample includes civilians ages 26-64 in the years 2010-2015. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, ** p<0.05, *

p<0.1). Controls include demographic controls (female, black, white, asian, other, hispanic), year FEs, age FEs, PUMA FEs, and PUMA-specific time trends.

58

Table A.4: Heterogeneity in Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Health Insurance Coverage by Observable Characteristics: Pooled

Income Groups

(1) (2) (3) (4) (5) (6)

VARIABLES Non-minority Minority

Childless

Adult

Has Child

Under 18 HSG or less

Some College

or More

Post x (E*+ M*) 0.0816*** 0.0956 0.1113** 0.1561*** 0.1068*** 0.1259***

(0.0272) (0.0615) (0.0467) (0.0391) (0.0383) (0.0443)

Expansion x Post 2.0825*** 2.1197*** 2.5898*** 1.5787*** 3.3311*** 1.4175***

(0.3129) (0.6153) (0.3279) (0.4129) (0.6044) (0.2235)

Exp x Post x (E*+M*) 0.1997*** 0.1482* 0.2008*** 0.0937** 0.1603*** 0.1370***

(0.0429) (0.0744) (0.0538) (0.0453) (0.0555) (0.0483)

Observations 6,537,661 2,955,570 6,266,853 3,226,378 3,602,122 5,891,109

R-squared 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Pre-ACA DV Mean 85.56 69.39 78.07 83.19 68.73 87.03

Non-Exp States 83.85 65.85 76.43 80.82 66.79 85.69

Expansion States 87.35 72.39 79.68 85.50 70.80 88.26

(7) (8) (9) (10) (11) (12)

VARIABLES Male Female

Young

(26-44)

Near Elderly

(45-64) Married Not Married

Post x (E*+M*) 0.1322*** 0.1198** 0.1036*** 0.1463*** 0.1648*** 0.0525

(0.0346) (0.0489) (0.0374) (0.0456) (0.0356) (0.0509)

Expansion x Post 2.5490*** 1.9753*** 2.4257*** 2.0985*** 1.6938*** 2.7965***

(0.3216) (0.3480) (0.3848) (0.2729) (0.2810) (0.5360)

Exp x Post x (E*+M*) 0.1662*** 0.1687*** 0.1608*** 0.1721*** 0.1224*** 0.2113***

(0.0383) (0.0584) (0.0484) (0.0508) (0.0388) (0.0661)

Observations 4,629,672 4,863,559 4,167,626 5,325,605 5,875,157 3,618,074

R-squared 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Pre-ACA DV Mean 77.53 82.16 75.25 84.26 86.02 71.17

Non-Exp States 75.75 80.13 72.79 82.82 84.75 68.16

Expansion States 79.25 84.16 77.61 85.68 87.29 74.02

Notes: Dependent variable is health insurance coverage and each column corresponds to a separate subsample. “Non-

minority” indicates white non-Hispanic. “Has Child Under 18” refers to individuals with at least one child living in

the same household who is under 18. “HSG or less” indicates that a high-school degree (including GED) or less is the

highest educational attainment. “Male” refers to gender. “Young” are ages 26-44. “Married” refers to marital status.

(E*+M*) refers to the share of individuals in a PUMA that are uninsured and 0-399% FPL in 2010-13. All shares are

standardized to have a mean of 0. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, **

p<0.05, * p<0.1). Controls include demographic controls (female, black, white, asian, other, hispanic), year FEs, age

FEs, PUMA FEs, and PUMA-specific time trends.

59

Table A.5: Effect of Potential Medicaid/Exchange Enrollment and

Medicaid Expansion on Not in the Labor Force by Observable Characteristics: Pooled Income

Groups

(1) (2) (3) (4) (5) (6)

VARIABLES Non-minority Minority

Childless

Adult

Has Child

Under 18 HSG or less

Some College

or More

Post x (E*+M*) -0.0013 0.0169 -0.0080 0.0253** 0.0176 -0.0015

(0.0133) (0.0130) (0.0169) (0.0125) (0.0228) (0.0115)

Expansion x Post 0.0939 0.1900 0.2357 -0.0853 0.2742 0.0791

(0.2005) (0.2314) (0.1788) (0.1743) (0.2456) (0.1607)

Exp x Post x (E*+M*) -0.0047 -0.0105 -0.0000 -0.0093 -0.0044 -0.0179

(0.0242) (0.0215) (0.0205) (0.0189) (0.0278) (0.0152)

Observations 6,537,661 2,955,570 6,266,853 3,226,378 3,602,122 5,891,109

R-squared 0.9996 0.9998 0.9997 0.9996 0.9998 0.9996

Pre-ACA DV Mean 21.58 24.38 25.39 17.38 30.60 17.41

Non-Exp States 22.34 25.39 26.61 17.32 31.34 17.82

Expansion States 20.78 23.52 24.21 17.43 29.82 17.03

(7) (8) (9) (10) (11) (12)

VARIABLES Male Female

Young

(26-44)

Near Elderly

(45-64) Married Not Married

Post x (E*+M*) 0.0103 -0.0012 0.0116 -0.0002 0.0131 -0.0040

(0.0150) (0.0149) (0.0143) (0.0145) (0.0120) (0.0230)

Expansion x Post -0.0472 0.3126* 0.0724 0.1766 0.0667 0.1943

(0.2038) (0.1818) (0.1784) (0.1768) (0.1502) (0.2152)

Exp x Post x (E*+M*) -0.0168 0.0067 -0.0118 0.0021 -0.0173 0.0109

(0.0177) (0.0210) (0.0198) (0.0199) (0.0171) (0.0292)

Observations 4,629,672 4,863,559 4,167,626 5,325,605 5,875,157 3,618,074

R-squared 0.9996 0.9998 0.9997 0.9997 0.9996 0.9998

Pre-ACA DV Mean 17.14 27.82 17.74 27.11 21.23 24.45

Non-Exp States 17.94 28.53 18.12 28.19 21.77 25.60

Expansion States 16.35 27.12 17.37 26.05 20.70 23.35

Notes: Dependent variable is not in the labor force and each column corresponds to a separate subsample. “Non-

minority” indicates white non-Hispanic. “Has Child Under 18” refers to individuals with at least one child living in

the same household who is under 18. “HSG or less” indicates that a high-school degree (including GED) or less is the

highest educational attainment. “Male” refers to gender. “Young” are ages 26-44. “Married” refers to marital status.

(E*+M*) refers to the share of individuals in a PUMA that are uninsured and 0-399% FPL in 2010-13. All shares are

standardized to have a mean of 0. Robust standard errors are in parentheses and are clustered by state (*** p<0.01, **

p<0.05, * p<0.1). Controls include demographic controls (female, black, white, asian, other, hispanic), year FEs, age

FEs, PUMA FEs, and PUMA-specific time trends.